Robot Path Planning Using Modified Artificial Bee Colony Algorithm

Nayyar, Anand; Nguyễn, Như Gia; Kumari, Rajani; Kumar, Sandeep

doi:10.1007/978-981-13-9920-6_3

Cited by 27 publications

(15 citation statements)

References 22 publications

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“…The shortcomings of BFOA were compounded by the local optima and fixed step length. However, poor convergence was not a challenge unique to BFOA but all swarm intelligence-based algorithms, as noted by Nayyar et al [42], Tang et al [20], and Fan et al [19]. The criticism of the BFOA by Chen et al and Chen and Zhang contrasts with other studies, arguing that BFOA demonstrated superior performance to GA and PSOA [167].…”

Section: Bacterial Foraging Optimization Algorithms (Bfoa)mentioning

confidence: 97%

“…Hi et al [37] noted that the incorporation of the algorithms helped to reduce the cost of the calculations, improved the accuracy of the measurements, and minimized the local minimum problem; this was evident in the optimization of the grid integrated renewable energy [39], intelligent allocation of water resources in multi-reservoir systems [40], optimization of the agricultural machinery path planning [41,42], and detection of disease infestation in plant leaves [43,44]. The suitability of all BIAs in optimizing IoT systems in agriculture is constrained by the complexity and uncertainty of ubiquitous IoT services.…”

Section: Introductionmentioning

confidence: 99%

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Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Maraveas

Arvanitis

Bartzanas

et al. 2022

Arch Computat Methods Eng

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The article reviewed the four major Bioinspired intelligent algorithms for agricultural applications, namely ecological, swarm-intelligence-based, ecology-based, and multi-objective algorithms. The key emphasis was placed on the variants of the swarm intelligence algorithms, namely the artificial bee colony (ABC), genetic algorithm, flower pollination algorithm (FPA), particle swarm, the ant colony, firefly algorithm, artificial fish swarm, and Krill herd algorithm because they had been widely employed in the agricultural sector. There was a broad consensus among scholars that certain BIAs' variants were more effective than others. For example, the Ant Colony Optimization Algorithm and genetic algorithm were best suited for farm machinery path optimization and pest detection, among other applications. On the contrary, the particle swarm algorithm was useful in determining the plant evapotranspiration rates, which predicted the water requirements and optimization of the irrigation process. Despite the promising applications, the adoption of hyper-heuristic algorithms in agriculture remained low. No universal algorithm could perform multiple functions in farms; different algorithms were designed to perform specific functions. Secondary concerns relate to data integrity and cyber security, considering the history of cyber-attacks on smart farms. Despite the concerns, the benefits associated with the BIAs outweighed the risks. On average, farmers can save 647–1866 L on fuel which is equivalent to US$734-851, with the use of GPS-guided systems. The accuracy of the BIAs mitigated the risk of errors in applying pesticides, fertilizers, irrigation, and crop monitoring for better yields.

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Section: Bacterial Foraging Optimization Algorithms (Bfoa)mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Maraveas

Arvanitis

Bartzanas

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…As robots operate under an increasingly complex environment, these algorithms cannot well address the environment-matching problems. Many heuristic algorithms or hybrid algorithms, such as the ant colony algorithm [10][11][12], simulated annealing algorithm [13][14][15], and rapidly exploring random tree algorithm [16][17][18] have been developed in succession. In recent years, some new biological intelligence algorithms have been proposed [19][20][21][22][23][24][25], which greatly expands the alternative methods of path planning.…”

Section: Related Workmentioning

confidence: 99%

A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

Tao

Wen

et al. 2022

Electronics

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A wall surface inspection robot mainly relies on the inertial measurement unit and global positioning system (GPS) signal during intelligent wall surface inspection. The robot may encounter incorrect positioning under a GPS-denied environment, easily triggering safety accidents. In order to obtain a path suitable for the safe work of the robot wall surface inspection robot in a GPS-denied environment, a global path-planning method for wall surface inspection robots was proposed based on the improved generic algorithm (GA). The influencing factor for GPS signal strength was introduced into the heuristic function in path planning for GA to address the aforementioned problem. Using the PSO algorithm, GA was initialized and the influencing term of GPS signal was introduced into the fitness degree function so as to achieve point-to-point path planning of vertical wall surface inspection robot. Path angle and probability of intersection and variation was taken into account for better path planning capability. Finally, the simulation experiments were performed. The generated path using the improved GA was found to avoid the blind area of the GPS signal. The algorithm proposed has a good performance with average convergence times of 35.9 times and an angle of 55.88° in simple environment. Contrary to the traditional GA and PSO algorithm, the method showed advantages in terms of the convergence rate, path quality, path angle change, and algorithm stability. The research presented in this article is meaningful and relatively sufficient. The simulation test is also quite convincing. The proposed method was proved to be effective in global path planning for a wall surface inspection robot.

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“…Dereli and Koker (2020) used the ABC approach to solve the inverse kinematics of a seven-degrees-of-freedom robotic arm. Eventually, Nayyar et al (2020) presented a modified ABC algorithm to analyze the robot path planning problem. This research work develops authors' previous works (Sahnehsaraei et al 2014;Mahmoodabadi et al 2013;2018; by presenting an optimal adaptive robust control method based on the gradient descent method and the multi-objective artificial bee colony.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of an adaptive robust controller using a multi-objective artificial bee colony algorithm for an inverted pendulum system

Mahmoodabadi

Lakmesari

2022

Trans. Can. Soc. Mech. Eng.

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In this paper, a multi-objective artificial bee colony (MOABC) optimization algorithm is utilized to improve the performance of an adaptive robust control technique. This approach is implemented on an inverted pendulum system. More precisely, the proposed controller is a combination of a decoupled sliding mode controller (DSMC) and adaption laws based on the gradient descent approach. In order to achieve the optimum control operation, the MOABC, as a novel meta-heuristic method simulated from the smart foraging activity of honey bee groups, is employed to optimize the coefficients of the suggested controller. In this regard, the objective functions are determined as the integral time of the absolute value of the pole angle and cart position errors. Finally, the time responses of the system states and control effort are presented to prove the effectiveness and feasibility of the suggested strategy compared to other contemporary studies referenced in the paper.

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Robot Path Planning Using Modified Artificial Bee Colony Algorithm

Cited by 27 publications

References 22 publications

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

Optimal design of an adaptive robust controller using a multi-objective artificial bee colony algorithm for an inverted pendulum system

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